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\title[Frequency-adaptive Reclustering]{An Reclustering Frequency-adaptive Information Routing Approach for
Efficient Data Storage in Wireless Sensor Network}%

\author{Tianlong Yun}
\address[T.~Yun]{China Academy of Telecommunication Research \\
Beijing, P.R.China}%
\email[T.~Yun]{yuntianlong2002@gmail.com}

\author{Wenjia Niu}
\address[W.~Niu]{Institute of Acoustics \\
Chinese Academy of Sciences, No.21 Beisihuan West Street \\
Beijing, P.R.China}%
\email[W.~Niu]{niuwj@hpnl.ac.cn}

%\thanks{Thanks to \ldots}%
\subjclass{Wireless Sensor Networks}%
\keywords{Wireless Sensor Networks, Cluster,DCS}%
\date{\svndate}%

\begin{abstract}
The wireless sensor network(\emph{WSN}) enables the information
gathering from a variety of environment. Due to limit energy and
storage resource of sensor node, it is an essential research issue
for \emph{WSN} to develop efficient and effective information
routing mechanisms for data storage and retrieval. Recently, the
clustering technique has been widely utilized for many
low-energy-based heuristic routing approaches(e.g. \emph{LEACH},
\emph{Gupta} and \emph{CHEF}) in \emph{WSN}. In most of these
approaches, the reclustering will generally be set to a pre-designed
fixed frequency $f$. However, in real \emph{WSN} applications, the
network context(e.g. energy and load) may dynamically change, which
will affect the clustering effect and further cluster-based routing
performance if we do not make prompt adjustments on the $f$. In this
paper, we propose a reclustering frequency-adaptive routing
approach, in which the $f$-aware newtork context are modeled and
corresponding $f$ adjustment mechanism is further developed.
Furthermore, a $f$-based heuristic routing algorithm is presented as
well. The case study and experimental evaluations demonstrate the
capability of the proposed approach.
\end{abstract}


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\section{Introduction}\label{sec-intro}
Wireless sensor network(\emph{WSN}) is designed to efficiently
collect information from many physical environments by densely
deploying lots of sensor nodes~\cite{if2002ssn}. In \emph{WSN},
sensing data can be disseminated among different sensor nodes, while these nodes are usually
quite small with very limited computational,
storage and energy resource.

In \emph{WSN}, common nodes are responsible for collecting underlying sensing data, while the base station is
responsible for retrieving these data. Hence, for information
discovery, how to
realize effective routing-based data dissemination among common nodes, then how to realize
efficient data transmission from common node to base station become a critical issue. Many efforts have
been made for this research issue. The early but classical approach is the data-centric storage
(\emph{DCS})~\cite{shenker2003data}, in
which different type of raw sensor data can be abstracted into
high-level event type and certain node will be specified as a
``storage node'' with a unique \emph{ID}. Each \emph{storage node}
will manage a set of subordinate nodes, which will periodically
report its data to \emph{storage node}. Then the \emph{storage node} will transmit their data to base station.
This will form an efficient
top-down hierarchical architecture. However,``hotspot'' problem may be
generated~\cite{aly2005zs}. More specially, one \emph{storage node} will not
replaced by other high-energy \emph{storage node} or even common node.
Hence, some \emph{storage node} will run out its energy and dies at early stage, which will further
affect the data transmission to base station, in which
such \emph{storage node} can be regarded as \emph{hotspot}.

Lately, to solve the \emph{hotspot} problem in \emph{DCS},
clustering technique begins to be adopted for dynamic and efficient data routing.
Typical work involves the \emph{LEATH}~\cite{heinzelman2002leath},
\emph{Gupta}~\cite{gupta2005cef} and \emph{CHEF}~\cite{kim2008chef}.
In (\emph{LEATH}), all the common nodes will randomly select themselves as \emph{storage node},
then remaining common nodes will join their nearest \emph{storage nodes}.
Hence, this dynamic selection of \emph{storage node} can balance the energy consuming of \emph{storage node}.
Compared with (\emph{LEATH}), the \emph{Gupta} considered network context to heuristically select
\emph{storage nodes} rather than random selection.
More specially, those nodes with higher energy and central location will be more likely to be
selected as \emph{storage nodes}.
In this method, both the clustering and selection work will be put together in base station.
\emph{CHEF} aims to
improve \emph{Gupta} by distributing clustering and selection function at
each local common nodes rather than base station.

Through analysis we found that, \emph{LEACH},\emph{Gupta} and \emph{CHEF} provide effective clustering-based
solutions for eliminating the hotspot problem in data storage and retrieval of \emph{WSN}. However,
in the real-world \emph{WSN} application, dynamical irregular data collecting and requests always appear.
For instance, in a \emph{WSN}-based
intelligent building application, the sensing frequency of temperature at working hours may be higher than
that at off-duty hours.
Hence, these context contains important knowledge which may affect the
performance of clustering-based data storage and retrieval.

\subsection{Scenario Example}
Consider a very simple scenario where there are only 3 nodes
deployed. And a base station which used to initiate data request. In
the 3-nodes network, only storage have to communicate with base
station. The storage node cost more energy because it is in charge
of all the data exchange with base station, moreover, base station
is very far away from \emph{WSN} which make the energy consumption
even large. Also we must notice that the clustering itself cost a
lot of energy especially in a huge network for a plenty of
communication between the nodes or between nodes and base station.

First, we assume that clustering is performing faster than it should
be. As we mention above, at off-duty hours when there are barely no
data request, the frequency of clustering should be also slow down.
Otherwise, because of the feature \emph{Gupta} and \emph{CHEF}, new
\emph{storage node} will be selected even a slight decrease of
energy in current \emph{storage node}.

Another situation is that the frequency of clustering is too low. In
this case, the change of network context cannot be used immediately.
A storage could run out its energy before the next round of
clustering. Even the nodes is not dead, the remain energy may not
sustain for another serval rounds.

we can conclude that clustering should be done at a right time in
term of the remain energy of nodes. 




\section{Preliminaries} \label{sec-preliminaries}

\section{Method} \label{sec-method}

\section{Experiment and Analysis} \label{sec-experiment}

\section{Conclusions} \label{sec-conclusions}

\section*{Acknowledgement}

The authors would like to thank \ldots

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